Kenton W. Ross

Review of FEWS NET biophysical monitoring requirements

The Famine Early Warning System Network (FEWS NET) provides monitoring and early warning support to decision makers responsible for responding to famine and food insecurity. FEWS NET transforms satellite remote sensing data into rainfall and vegetation information that can be used by these decision makers. The National Aeronautics and Space Administration has recently funded activities to enhance remote sensing inputs to FEWS NET. To elicit Earth observation requirements, a professional review questionnaire was disseminated to FEWS NET expert end-users; it focused upon operational requirements to determine additional useful remote sensing data and, subsequently, to assess whether such data would be beneficial as FEWS NET biophysical supplementary inputs. The review was completed by over 40 experts from around the world. Reviewers were asked to evaluate the relative importance of environmental variables and spatio-temporal requirements for Earth science data products, in particular for rainfall and vegetation products. The results showed that spatio-temporal resolution requirements are complex and need to vary according to place, time, and hazard; that high resolution remote sensing products continue to be in demand; and that rainfall and vegetation products are valued as data that provide actionable food security information.

High Granularity Remote Sensing and Crop Production over Space and Time: NDVI over the Growing Season and Prediction of Cotton Yields at the Farm Field Level in Texas

Remote sensing has been applied to agriculture at very coarse levels of granularity (i.e., national levels) but few investigations have focused on yield prediction at the farm unit level. Specific aims of the present investigation are to analyze the ability of Moderate Resolution Imaging Spectroradiometer (MODIS) data to predict cotton yields in two highly homogeneous counties in west Texas. In one study county > 90% of cotton grown is irrigated, while the other study county 40 miles south has >85% non-irrigated cotton. Regression analysis by day from April to November at the county and farm levels reveals a highly significant ability for MODIS to predict cotton yields. R values ranged from 0.90 to 0.98 for irrigated cotton and 0.80 to . 90 for non-irrigated cotton practices. The objective in future studies is to algorithmically extend these analyses to the ~300 million acres of arable land under cultivation in the United States.